The AI/ML techniques and relevant applications are being increasingly adopted by the wider industries and proved to be successful. These are now being applied to telecommunication industry including mobile networks. Although AI/ML techniques in general are quite mature nowadays, some of the relevant aspects of the technology are still evolving while new complementary techniques are frequently emerging. The AI/ML techniques can be generally characterized from different perspectives including the followings:

• Learning methods: The learning methods include supervised learning, semi-supervised learning, unsupervised learning and reinforcement learning. Each learning method fits one or more specific category of inference (e.g. prediction), and requires specific type of training data. A brief comparison of these learning methods is provided in Table 4.1-1.

• Learning complexity: As per the learning complexity, there are Machine Learning (i.e. basic learning) and Deep Learning.
• Learning architecture: Based on the topology and location where the learning tasks take place, the AI/ML can be categorized to centralized learning, distributed learning and federated learning.
• Learning continuity: From learning continuity perspective, the AI/ML can be offline learning or continual learning.

Artificial Intelligence/Machine Learning (AI/ML) capabilities are used in various domains in 5GS, including management and orchestration (e.g. MDA, see TS 28.104) and 5G networks (e.g. NWDAF, see TS 23.288). The AI/ML-inference function in the 5GS uses the ML model for inference. Each AI/ML technique, depending on the adopted specific characteristics as mentioned above, may be suitable for supporting certain type/category of use case(s) in 5GS. To enable and facilitate the AI/ML capabilities with the suitable AI/ML techniques in 5GS, the ML model and AI/ML inference function need to be managed. The present document specifies the AI/ML management related capabilities and services, which include the following:

• ML training.